In recent years, an increasing amount of interest and research effort has been put toward intelligent or autonomous vehicles. With the continuous progress in autonomous technology, robot sensors are generating increasing amounts of real-world data. Autonomous vehicle research is highly dependent on the vast quantities of real-world data for development, testing and validation of algorithms before deployment on public roads. However, the cost of processing and analyzing these data, including developing and maintaining a suitable autonomous vehicle platform, regular calibration and data collection procedures, and storing the collected data, is so high that few research groups can manage it. Following the benchmark-driven approach of the computer vision community, a number of vision-based autonomous driving datasets have been released. Some existing datasets, however, may not be well generalized to different environments.
All referenced patents, applications and literatures throughout this disclosure are incorporated herein by reference in their entirety. For example, including the following references:                Z. Zhang, “A flexible new technique for camera calibration”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000; https://pdfs.semanticscholar.org/7fc6/2b438ca48203c7f48e216dae8633db74d2e8.pdf:        Andreas Geiger and Philip Lenz Karlsruhe Institute of Technology {geiger,lenz}@kit.edu; and Raquel Urtasun Toyota Technological Institute at Chicago rurtasun@ttic.edu        “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suit”, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012; www.cvlibs.net/publications/Geiger2012CVPR.pdf;        Andreas Geiger, Frank Moosmann, “Omer Car and Bernhard Schuster, “Automatic Camera and Range Sensor Calibration using a single Shot”, http://www.cvlibs.net/publications/Geiger2012ICRA.pdf;        Ji Zhang and Sanjiv Singh, “LOAM: Lidar Odometry and Mapping in Real-time”; www.cvlibs.net/projects/autonomous vision survey/literature/Zhang2014RSS.pdf:        Jesse Levinson, Sebastian Thrun Stanford Artificial Intelligence Laboratory {jessel,thrun}@stanford.edu; “Unsupervised Calibration for Multi-beam Lasers”; www.driving.stanford.edu/papers/ISER2010.pdf;        S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski. “A Database and Evaluation Methodology for Optical Flow”, IJCV, 92:1-31, 2011; https://vision.middlebury.edu/flow/floweval-ijcv2011.pdf; and        S. Morales and R. Klette, “Ground truth evaluation of stereo algorithms for real world applications”, In ACCV Workshops, volume 2 of LNCS, pages 152-162, 2010; https://pdfs.semanticascholar.org/254a/2d2967341d8d9bfa9c332e82c927e0d8a0d6.pdf        